Representation and analysis methods for spatial data visualization |
日時:3月6日(金)17:15-17:30
会場:第1イベント会場(6号館 334 多目的ホール)
【講演概要】Enabling insight into large and complex spatial datasets is the central theme in scientific data visualization research. Visualizing large spatial data sets requires efficient data representation methods, powerful analysis algorithms for data transformation and effective rendering and human-computer interaction methods to allow domain experts to gain insight from the data. We consider here spatial data representing continuous phenomena, such as scalar fields (terrains, 2D or 3D images, unstructured volume data sets, etc.), and multifields, collections of fields with different modalities. The talk will focus on the first two steps of the data visualization pipeline concerning compact and scalable representations for large-size spatial data sets, and data transformation methods based on abstracting characteristic features from the data. Specifically, we will review new approaches to data representation based on modular decomposition and discuss their scalability, and describe topology-based methods for data transformation to support visualization. Topology-based visualization is a vibrant area of research in the scientific visualization field. New trends and challenges in this area will be discussed. 【略歴】Leila De Floriani is a professor at the University of Maryland, College Park, USA. She has previously been a professor at the University of Genova (Italy), and she has also held positions at the University of Nebraska, Rensselaer Polytechnic Institute, and the Italian National Research Council. De Floriani is the 2020 President of the IEEE Computer Society. She has been a member of the Board of Governors of the IEEE Computer Society (CS) since 2017. She is a Fellow of IEEE, a Fellow of the International Association for Pattern Recognition (IAPR) as well as a Pioneer of the Solid Modeling Association. She is an IEEE Computer Society Golden Core Member and a member of the IEEE Honor Society IEEE-HKN. |